---
title: "Motor Vehicle Accidents in Victoria"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
orientation: rows
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(lubridate)
library(janitor)
library(plotly)
library(ggResidpanel)
library(broom)
library(knitr)
library(kableExtra)
```
```{r load-data}
accidents <- read_csv("data/ACCIDENT.csv") %>%
clean_names()
locations <- read_csv("data/ACCIDENT_LOCATION.csv") %>%
clean_names()
nodes <- read_csv("data/NODE.csv") %>%
clean_names()
persons <- read_csv("data/PERSON.csv") %>%
clean_names()
vehicles <- read_csv("data/VEHICLE.csv") %>%
clean_names()
```
```{r create-year-hour-day}
accidents <- accidents %>%
mutate(accidentdate = dmy(accidentdate),
Year = year(accidentdate),
Hour = hour(accidenttime),
Weekday = wday(accidentdate,
label = TRUE,
abbr = FALSE))
```
Part 2 {data-icon="fa-battery-half"}
=====================================
Row {.tabset data-height=500}
------------
### **Deaths by speed zone**
```{r accidents-by-speed-zone}
accidents_by_speed_zone <- accidents %>%
count(speed_zone,
name = "Accidents")
```
```{r deaths-by-speed-zone}
deaths_by_speed_zone <- accidents %>%
group_by(speed_zone) %>%
tally(no_persons_killed,
name = "Deaths")
```
```{r deaths-per-accident-by-speed-zone}
deaths_by_accident <- accidents_by_speed_zone %>%
left_join(deaths_by_speed_zone) %>%
mutate(Deaths_by_accident = Deaths/Accidents)
```
```{r deaths-per-accident-plot}
deaths_by_accident %>%
mutate(speed_zone = as.numeric(speed_zone)) %>%
filter(speed_zone %in% seq(30, 110, 10)) %>%
ggplot(aes(y = Deaths_by_accident,
x = speed_zone)) +
geom_line() +
labs(x = "Speed Zone",
y = "Deaths by Accident")
ggplotly()
```
Row {.tabset data-height=500}
------------
### **Death rate by year of vehicle manufacture**
```{r join-person-and-vehicle}
person_vehicle <- persons %>%
left_join(vehicles)
```
```{r total-people-involved-in-accidents-per-manufature-year}
person_vehicle_total <- person_vehicle %>%
group_by(vehicle_year_manuf) %>%
tally(name = "Persons",
sort = TRUE)
person_vehicle_deaths <- person_vehicle %>%
filter(inj_level_desc == "Fatality") %>%
group_by(vehicle_year_manuf) %>%
tally(name = "Fatalities",
sort = TRUE)
```
```{r join-total-and-deaths}
death_rate_by_year_manuf <- person_vehicle_total %>%
left_join(person_vehicle_deaths) %>%
mutate(death_rate = Fatalities/Persons) %>%
arrange(desc(vehicle_year_manuf))
```
```{r plot-death-rate-by-year-manuf}
manuf_year_death_rate <- death_rate_by_year_manuf %>%
filter(vehicle_year_manuf >= 1985 & vehicle_year_manuf < 3001)
p1 <- manuf_year_death_rate %>%
ggplot(aes(x = vehicle_year_manuf,
y = death_rate)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Year Manufactured",
y = "Death Rate")
ggplotly(p1)
```
### **Regression model**
```{r regression-model}
manuf_year_death_rate_lm <- lm(death_rate ~ vehicle_year_manuf, data = manuf_year_death_rate)
resid_panel(manuf_year_death_rate_lm, plot = "all")
```
### **Goodness of fit**
```{r}
tidy(manuf_year_death_rate_lm) %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
glance(manuf_year_death_rate_lm) %>%
kable() %>%
kable_styling(bootstrap_options = "striped")
```
Column {.sidebar data-width=350}
----
***
> **Findings**
1. Accidents become increasingly serious the faster the speed at which they occur. The risk of dying in an accident is nearly 13 times higher in a 110km/h zone (0.064 deaths/accident) than in a 40km/h zone (0.005 deaths/accident).
2. A person's risk of dying in an accident is positively correlated with the age of their vehicle; the newer the make, the less likely it is that a person will be killed in an accident. For every year older a vehicle is, the risk of dying if you have an accident in it increases by 0.0002 deaths per accident. The reason for this is improved safety standards for vehicles.
***
